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| 老年患者腹腔镜结直肠癌手术后肺部并发症的危险因素和列线图预测模型建立 |
| Risk factors and nomogram prediction model establishment for postoperative pulmonary complications in elderly patients undergoing laparoscopic colorectal cancer surgery |
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| DOI：10.12089/jca.2026.06.003 |
| 中文关键词: 结直肠癌 术后肺部并发症 老年 危险因素 列线图预测模型 |
| 英文关键词: Colorectal cancer Postoperative pulmonary complications Aged Risk factors Nomogram prediction model |
| 基金项目: |
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| 中文摘要: |
目的：分析老年腹腔镜结直肠癌手术患者发生术后肺部并发症（PPCs）的危险因素，并构建风险预测模型。
方法：收集2022年1月至2025年3月行腹腔镜下老年结直肠癌手术患者临床资料作为训练集。在训练集中，通过单因素及多因素Logistic回归分析筛选出腹腔镜下老年结直肠患者手术后发生PPCs的独立危险因素，并基于此构建列线图预测模型。另收集2025年4—12月的老年结直肠癌患者为测试集，采用受试者工作特征（ROC）曲线下面积（AUC）和校准曲线评估列线图预测模型在训练集和测试集中的预测性能，决策曲线分析（DCA）评估其临床价值。
结果：共纳入老年患者736例，训练集516例，测试集220例，其中训练集中有66例（12.8％）患者发生PPCs，测试集中有25例（11.4％）患者发生PPCs。多因素Logistic回归分析结果显示，男性、术前衰弱、术前系统性免疫炎症指数（SII）、术前红细胞分布宽度（RDW）、术前D-二聚体、术中低血压持续时间、手术时间为老年患者结直肠癌术后发生PPCs的危险因素。基于多因素分析结果构建列线图预测模型，训练集AUC为0.833（95%CI 0.774～0.892），测试集AUC为0.845（95%CI 0.757～0.932），校准曲线和DCA评估模型在训练集与测试集中均表现出良好的预测性能与临床实用价值。
结论： 本研究所构建的列线图预测模型可用于早期筛查具有PPCs高风险的患者群体，及时干预以改善其预后，为临床实践提供有益的决策支持。 |
| 英文摘要: |
Objective: To analysis the risk factors for postoperative pulmonary complications (PPCs) in elderly patients undergoing laparoscopic colorectal cancer surgery and developed a predictive model.
Methods: Clinical data of elderly patients who underwent laparoscopic colorectal cancer surgery from January 2022 to March 2025 were collected as the training set. In the training set, independent risk factors for PPCs in elderly patients undergoing laparoscopic colorectal cancer surgery were identified using univariate and multivariate logistic regression analysis. Based on these factors, a nomogram prediction model was constructed. Elderly patients with colorectal cancer from April 2025 to December 2025 were selected as the testing set. The predictive performance of the nomogram in both the training and testing sets was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and calibration curves. Decision curve analysis (DCA) was employed to assess its clinical utility.
Results: A total of 736 elderly patients were enrolled, the training set consists of 516 patients, while the testing set contains 220 patients, 66 patients (12.8%) in the training set and 25 patients (11.4%) in the testing set developed PPCs. Multivariate logistic regression analysis revealed that male, preoperative frailty, preoperative systemic immune-inflammation index (SII), preoperative red blood cell distribution width (RDW), preoperative D-dimer, duration of intraoperative hypotension, and operative time were independent risk factors for PPCs following elderly colorectal cancer surgery. A nomogram prediction model was constructed based on the results of multivariate analysis. The AUC of the model was 0.833 (95% CI 0.774-0.892) in the training set and 0.845 (95% CI 0.757-0.932) in the testing set. Calibration curves and DCA demonstrated that the model exhibited favorable predictive performance and clinical utility in both the training and testing sets.
Conclusion: The nomogram prediction model developed in this study can facilitate the early identification of patients at high risk for PPCs. This enables timely interventions to improve patient prognosis and provides valuable decision-making support for clinical practice. |
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